Download GIS and Homeland Defense - Wharton GIS Lab

University of Pennsylvania
Institute for Strategic Threat Analysis and Response (ISTAR)
Climate
Information Networks
Geopolitics
Homeland
defense
Infectious diseases
Geographic data
Remote sensors
Contents
Foreword — Don de Savigny, Luc Loslier, and Jim Chauvin
Preface — Don de Savigny, Lori Jones-Arsenault, and Pandu Wijeyaratne
Context
•The present state of GIS and future trends — Steven Reader
•GIS from a health perspective — Luc Loslier
•Spatial and temporal analysis of epidemiological data — Flavio Fonseca Nobre and Marilia Sa
Carvalho
Case studies from the South
•Towards a rural information system — David le Sueur, Sipho Ngxongo, Maria Stuttaford, Brian
Sharp, Rajendra Maharaj, Carrin Martin, and Dawn Brown
•A GIS approach to the determination of catchment populations around Local Health Facilities in
Developing Countries — H.M. Oranga
•GIS management tools for the control of tropical diseases: applications in Botswana, Senegal,
and Morocco — Isabelle Nuttall, D.W. Rumisha, T.R.K. Pilatwe, H.I. Ali, S.S. Mokgweetsinyana,
A.H. Sylla, and I. Talla
•The use of low-cost remote sensing and GIS for identifying and monitoring the environmental
factors associated with vector-borne disease transmission — S.J. Connor, M.C. Thompson, S.
Flasse, and J.B. Williams
•GIS for the study and control of malaria — Gustavo Bretas
•Spatial analysis of malaria risk in an endemic region of Sri Lanka — D.M. Gunawardena, Lal
Muthuwattac, S. Weerasingha, J. Rajakaruna, Wasantha Udaya Kumara, Tilak Senanayaka, P.
Kumar Kotta, A.R. Wickremasinghe, Richard Carter, and Kamini N. Mendis
•Diagnostic features of malaria transmission in Nadiad using remote sensing and GIS — M.S.
Malhotra and Aruna Srivastava
•Monitoring zoonotic cutaneous leishmaniasis with GIS — L. Mbarki, A. Ben Salah, S. Chlif, M.K.
Chahed, A. Balma, N. Chemam, A. Garraoui, and R. Ben-Ismail
•Use of RAISON for rural drinking water sources management — C.W. Wang
http://www.idrc.ca/acb/showdetl.cfm?&DID=6&Product_ID=495&CATID=15
Climate and Satellite Indicators to Forecast Rift Valley Fever
Epidemics in Kenya
Kenneth J. Linthicum, 1* Assaf Anyamba, 2* Compton J. Tucker, 2 Patrick W. Kelley, 1 Monica F.
Myers, 2 Clarence J. Peters 3
The best fit to the RVF outbreak data was achieved when equatorial
Pacific and Indian Ocean SST and NDVI anomaly data were used
together.
These data could have been used to successfully predict each of the
three RVF outbreaks that occurred between 1982 and 1998 without
predicting any false RVF events for an overall prediction of risk of
100%.
Predictive models that use either SOI and Indian Ocean or NDVI and
Indian Ocean anomaly data would have predicted all three RVF events
but falsely predicted either one or two disease events, respectively.
Science. 1999 Jul 16;285(5426):397-400.
The Sverdlovsk Anthrax Outbreak
New initiatives
Global and local syndromic surveillance—human and animal
Genomic characterization of species and strains of organisms
Global and local micro-organism surveillance
Distributed sensors
Massively networked information systems
Education
Research/Education Agenda
Dynamic Integration and Analysis of Data Sets
Data
Geographic
Syndromic
Microorganisms
Climate
Political alignments—state and non-state
Technology--theory
Sensors—hybrid systems
Network communication—artificial intelligence
Security—authentication, privacy
Conflict—asymmetric, multi-agent game theory
Education
(K-12)
undergraduates
graduate and professional students
practitioners
Policy